Azure Synapse Vs Snowflake: A Basic IntroductionMicrosoft Azure Synapse is a PaaS (Platform-as-a-Service) data platform. Synapse is, believe it or not, a very new Microsoft service, having been made public at the end of 2020. A synapse is intended to serve as a primary hub for connecting additional Azure capabilities. Synapse integrates with Apache Spark to handle streaming, artificial intelligence, and machine learning workloads in addition to standard SQL and Business Intelligence workloads. Snowflake is a data platform available as a SaaS (Software-as-a-Service). It is designed to work on any leading cloud providers to collect and consolidate data so that users can self-serve and query data using SQL to build reports and dashboards that drive business value. In addition, Snowflake manages all of the backend infrastructure and management that is often associated with cloud products.
Differences In ArchitecturesAzure Synapse is designed only for use on Azure Cloud. Storage and computing are distinct in Azure Synapse like Snowflake, and the platform is ANSI SQL compatible. Synapse studio, the UI for monitoring resources, performing activities, writing code, and managing user access, is at the heart of Azure Synapse. Azure Synapse employs a distributed query system that makes use of T-SQL typically used with Microsoft services. Azure Synapse also provides dedicated SQL pools, Serverless SQL pools, and Spark Pools. Snowflake as a data platform isn’t based on any particular database technology or big data software platform. Instead, Snowflake is a serverless, ANSI SQL-compliant system with totally distinct storage and compute processing layers. Furthermore, it’s built on a shared disk and shared-nothing architecture. It employs a centralized data store for all persistent data and makes it available to multiple compute nodes inside the platform. This data is then processed using MPP (Massively Parallel Processing) compute clusters known as data warehouses and stored locally. Snowflake automatically uses micro partitions to optimize and organize data into compressed columnar storage. This optimized data is stored in cloud storage, and Snowflake handles every aspect of file size, structure, compression, metadata, statistics and others. It can be accessed only through SQL query operations run within Snowflake. Snowflake’s warehouses each have their own autonomous computing cluster. As a result, warehouses do not share resources with other virtual warehouses. It means that data warehousing with Snowflake supports near-unlimited concurrency for both queries and users. Snowflake is also cloud-agnostic, running on all three main clouds, AWS, GCP, and Azure.
Scalability And Security ComplianceAzure Synapse offers fewer scalability features because it is not a native SaaS service. For example, serverless SQL Pools and Spark Pools are automatically scaled by default. At the same time, The user must manually adjust dedicated SQL server pools because Snowflake does not offer an auto-suspend/auto-resume option. Snowflake has auto-scaling and auto-suspend functionality for virtual warehouses during idle and busy periods. In addition, workloads are segregated independently to offer infinite concurrency because each Snowflake warehouse is on its unique computing cluster. Snowflake also features zero-copy cloning, allowing users to clone databases immediately without physically copying or storing the data. The common thread between them include:
- Both Snowflake and Synapse automatically encrypt data at rest, and each system includes RBAC (role-based access control) to manage users and privileges.
- Both solutions also provide vital security surrounding MFA (multi-factor authentication) and connection via VPNs (virtual private networks).
- Each tool is compatible with Azure Private Link. Furthermore, Snowflake and Synapse are compliant with SOC 1 Type II, SOC 2 Type II, HIPAA, GDPR, ISO 27001, Fedramp, and other standards.
Price Points – What Works Best For YouPricing with Synapse is complicated because there are far more possibilities. Computing resources, or DWUs, in Azure, start at $1.51 per hour for the smallest size and may reach $453 for the highest level. These expenses may be greatly lowered by acquiring reserved capacity. Serverless SQL pools, on the other hand, charge a flat cost of $5.65 per TB of data handled. Azure storage costs more than Snowflake storage, at roughly $26 per TB per month. One important thing to note is that Snowflake charges for computing on a per-minute basis and Azure Synapse charges on an hourly basis. It means that if query execution in Snowflake takes 3 minutes, users pay for 3 minutes of computing. In contrast, even if Azure Synapse is only active for 30 minutes, consumers are still required to pay for a complete hour. This explains why there appears to be such a significant disparity in the pricing models of both systems. Snowflake pricing is straightforward because it is dependent on the utilization of separate warehouses. Snowflake compute resources are sized X-Small, Small, Medium, Large, X-Large and further. These sizes differ significantly in cost and the number of servers/clusters. The cost of an X-small Snowflake warehouse begins at about 0.0003 credits per second or one credit per hour. Snowflake calculates credit costs based on the business tier selected. Snowflake Standard Edition on-demand pricing starts at $2 per credit. In addition, Snowflake offers on-demand pricing and reserved plans based on usage calculated on a per-second basis. Storage within Snowflake is charged at a flat rate of $40 per month for each TB for on-demand customers and $23 per TB for upfront customers.
The Essential Difference Between Azure Synapse And SnowflakeThe primary distinction between Snowflake and Synapse is that Synapse is designed to serve as an analytics layer on top of Azure Data Lake and a data warehouse for analytics workloads. As a result, Synapse, at its heart, connects seamlessly with other Azure services such as Github, Azure DevOps, Azure Data Factory, Power BI, and many others. Azure Synapse, is exceptionally capable of handling ML, AI, and streaming workloads because of its native Spark and Delta Lake connections. On the other side, Snowflake is actually built for traditional business intelligence workloads, and here is where it thrives. Snowflake does assist in the different areas indicated. However, when it comes to various workloads, Synapse is far more resilient. Snowflake’s functionality in these areas is provided via third-party partners and connectors. Because it has additional bells and whistles, Synapse is far more complicated and has a much higher learning curve. On the other hand, Snowflake is intended to be as easy as possible, allow infinite scale, and bring immediate benefit straight out of the box. As a result, no all-encompassing unicorn solution can handle everything. Snowflake performs certain things better than Synapse and vice versa. Snowflake and Azure Synapse are both excellent solutions. Therefore, while deciding between them, the use case and business objectives should be prioritized. E-Connect can help you select the right Data Warehouse and provide cloud migration services to fit business requirements. E-Connect is a Data Engineering and Data Analytics company with expertise in helping companies set up data warehousing with Snowflake and Azure Synapse. So, get in touch with us to discuss any of your Data warehousing, Cloud migration or Cloud integration requirements.